DaedalMap World Development Indicators

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint=true, so description is not required to restate it. The description adds no behavioral traits beyond annotations; 'Free discovery' is marketing fluff.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise with two short sentences. No unnecessary words except possibly 'Free discovery', but overall efficient and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no parameters and no output schema, the description adequately communicates the return value. Lacks details on pagination or limits, but for a simple listing tool it suffices.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters exist, and schema coverage is 100%. Baseline score of 4 is appropriate as description does not need to add parameter details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns a list of data packs on DaedalMap, using specific verb 'returns' and resource 'live agent-ready data packs'. However, it does not explicitly differentiate from siblings like get_pack or query_dataset, though the scope is implied.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives such as get_pack or query_dataset. The description lacks context for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations provide readOnlyHint=true, and the description adds value by listing the specific return contents (metadata, coverage, freshness, tool guidance, first-query examples) and noting it is 'Free discovery'. It does not contradict annotations; it enriches the behavioral context without needing to restate the read-only nature.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no filler. The first sentence front-loads the purpose and return contents, the second gives the when-to-use guidance. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool with no output schema, the description fully covers what the agent needs: what it returns, when to call it, and what to expect (paste-ready query). It is complete enough to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 100% coverage for the single parameter pack_id, with a clear description that it comes from get_catalog. The tool description adds no additional meaning beyond restating 'for one pack', so the baseline of 3 applies since the schema fully documents the parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (returns detailed metadata, coverage, freshness, guidance, and examples) and the resource (one pack). It distinguishes from siblings by explicitly targeting one pack and by mentioning 'preferred canonical tool guidance' and 'first-query examples', which are unique to this tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly instructs to 'Call this before querying a new pack', providing a concrete trigger condition. However, it does not explicitly name alternatives or state when not to use it, though the context implies get_catalog is for listing and query_dataset for actual data.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnlyHint annotation, the description discloses significant behavioral details: the response contains 'when to use it, what it refuses, a working example, effective access limits, important outputs, provenance fields, recommended next calls, and the shared natural-language-to-strict-JSON interaction contract.' This goes beyond the annotation and gives the agent a full picture of the tool's behavior and output breadth. No contradictions detected.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the core purpose. The second sentence, while listing many return items, is a single efficient list that avoids redundancy. Every sentence earns its place—there is no filler or repeated schema information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a meta-tool with one parameter and no output schema, the description is complete: it explains what the tool does, what it returns, and how to invoke it in a workflow. It covers usage context and expected content, leaving no significant gaps for an agent to guess.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already describes the sole parameter 'tool_name' as 'Exact tool name from tools/list,' so schema coverage is 100%. The description reinforces this by mentioning 'Use tools/list to discover names' but adds no new semantic information about the parameter beyond what the schema provides. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Free blind-caller guidance for one tool visible on this MCP facade.' It uses a specific verb ('guidance') and resource ('one tool'), and distinguishes itself from sibling data-access tools like get_catalog and query_dataset by being a meta-help tool. The list of returned content (when to use, what it refuses, etc.) further clarifies its unique role.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance is provided: 'Use tools/list to discover names, then call this before an unfamiliar tool.' This gives a clear workflow, specifies when to use the tool, and implicitly names the alternative (tools/list) for discovery. It also indicates the tool should be used proactively for unknown tools, leaving no ambiguity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true, so safety is covered. The description adds context like the pack list and the API contract reference, but does not describe response format, pagination behavior, or any rate limits. With annotations bearing the safety burden, this is adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences: the first front-loads the core purpose and contract, the second provides actionable context (pack lists and pricing). There is no filler; every clause serves a purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 8 optional parameters, nested objects, no output schema, and only readOnlyHint as annotation, the description gives a reasonable entry point by referencing the API contract and pack list. However, it omits details on response format, error handling, or how filters/metrics interplay, which an agent might need for confident invocation. It is adequate but not thorough.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents each parameter. The description adds value beyond the schema by providing concrete examples like 'earthquakes_events' and mentioning 'Use event_count for aggregate counts when supported', which clarifies intended usage for metrics. However, it does not systematically elaborate on each parameter beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb (query) and resource (dataset) with clear scope: direct source_id or pack_id access. It also references the API contract and lists free and paid packs, which helps distinguish it from siblings like get_pack or get_catalog, which likely provide metadata rather than data queries.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'for direct source_id or pack_id access' implies usage when the caller already knows the ID, and the listing of packs suggests when it's applicable. However, it does not explicitly state when to use alternatives (e.g., get_catalog for listing, get_pack for pack metadata) or when not to use this tool, leaving the routing partly implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.